Understanding the functional impact of copy number alterations in breast cancer
using a network modeling approach
Sriganesh Srihari1*,#, Murugan Kalimutho5# , Samir Lal2 , Jitin Singla3 , Dhaval Patel3, Peter T. Simpson2,4 , Kum Kum Khanna5 and Mark A. Ragan1
1Institute for Molecular Bioscience, The University of Queensland, St. Lucia, QLD 4072, Australia
2 The University of Queensland, UQ Centre for Clinical Research, Brisbane, QLD 4029, Australia
3 Indian Institute of Technology Roorkee, Roorkee, Uttarakhand 247667, India
4 The University of Queensland, School of Medicine, Brisbane, QLD 4006, Australia
5 QIMR-Berghofer Institute of Medical Research, Brisbane, QLD 4006, Australia
*Contact: sriganesh [dot] m [dot] s [at] gmail [dot] com
# joint first authors
Please cite: Srihari S, Kalimutho M et al., Understanding the functional impact of copy number alterations in breast cancer using a network modelling approach. Molecular Biosystems 2016; 10.1039/C5MB00655D.
If you are looking to follow up on BRF2 -- e.g. by designing a small-molecular inhibitor, or by testing BRF2 in other cell line and in vivo models -- we would love to hear from you, and be willing to help you further with any bioinformatic analysis that may be required. (Contact: sriganesh [d] m [d] s [at] gmail [d] com).
Supplementary figures and data
1/ Survival plots for k = 8, 9, 10 clusters using cis- , trans- and cis- and trans-associated genes
Figure 2 of manuscript: Kaplan-Meier plots of disease-specific survival (truncated at 15 years) for clusters identified using cis- (917), trans- (663) and by combining cis- and trans-associated (1527) genes (arranged horizontally) for k = 8, 9, 10 clusters (arranged vertically) from the Discovery dataset (998 tumours). Log-rank test p-value in each of the cases was significant (p < 0.0001). The cluster numbers and colours are not comparable across plots.
Figure 2i: Survival proportions using cis- and trans-associated genes for k = 10
2/ Cytoscape network file (.cys) of trans-associated genes along with their Gene Ontology enrichment information
3/ Experimental validation of RFC4 as a potential biomarker.
The effect is more prominent in ER-negative tumours compared to ER-positive tumours, indicating that RFC4 could be a marker for ER-negative/aggressive tumours. However, since normal MFC10A cells are also affected, targeting RFC4 could induce some toxicity.
4/ Experimental validation of BRF2 as a therapeutic target for ER-/HER2+ breast tumours.
BRF2 shows higher protein expression for ER-negative/HER2-enriched tumours compared to normal MCF10A cells, and the knockdown of BRF2 induces significantly more cell death in MDA-MB-453 and -361 cells compared to normal cells. Therefore, BRF2 could be a very good therapeutic target to pursue.
5/ Source codes (in C++ and R)
Australian National Health and Medical Research Council (NHMRC) grant 1028742 to PTS and MAR.